AI RESEARCH
EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models
arXiv CS.CL
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ArXi:2603.12252v1 Announce Type: cross Recently, Multimodal Large Language Models (MLLMs) have been widely integrated into diffusion frameworks primarily as text encoders to tackle complex tasks such as spatial reasoning. However, this paradigm suffers from two critical limitations: (i) MLLMs text encoder exhibits insufficient reasoning depth. Single-step encoding fails to activate the Chain-of-Thought process, which is essential for MLLMs to provide accurate guidance for complex tasks. (ii) The guidance remains invariant during the decoding process.